In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figure.figsize']=20,10
from keras.models import Sequential
from keras.layers import LSTM,Dropout,Dense
from sklearn.preprocessing import MinMaxScaler
import yahoo_finance
from datetime import datetime
from pandas_datareader import data as pdr
import plotly.express as px
import plotly.graph_objects as go
In [3]:
end = datetime.now()
start = datetime(2016, 1, 1)
In [4]:
#Entering the Stock name.
stock = input('Enter Stock name ').upper()

Enter Stock name amzn
In [5]:
#Extracting the data from Yahoo and converting into a csv file.
df = pdr.get_data_yahoo(stock, start, end)
df = df.to_csv(f'{stock}.csv')
df = pd.read_csv(f'{stock}.csv')
df
Out[5]:
Date High Low Open Close Volume Adj Close
0 2015-12-31 687.750000 675.890015 686.080017 675.890015 3749600 675.890015
1 2016-01-04 657.719971 627.510010 656.289978 636.989990 9314500 636.989990
2 2016-01-05 646.909973 627.650024 646.859985 633.789978 5822600 633.789978
3 2016-01-06 639.789978 620.309998 622.000000 632.650024 5329200 632.650024
4 2016-01-07 630.000000 605.210022 621.799988 607.940002 7074900 607.940002
... ... ... ... ... ... ... ...
1364 2021-06-03 3214.439941 3184.030029 3204.229980 3187.010010 2398300 3187.010010
1365 2021-06-04 3221.000000 3198.810059 3212.000000 3206.219971 2245700 3206.219971
1366 2021-06-07 3208.000000 3172.199951 3197.330078 3198.010010 2215800 3198.010010
1367 2021-06-08 3279.530029 3218.010010 3222.610107 3264.110107 3405900 3264.110107
1368 2021-06-09 3297.580078 3270.699951 3272.870117 3281.149902 2439123 3281.149902

1369 rows × 7 columns

In [6]:
df.describe()
Out[6]:
High Low Open Close Volume Adj Close
count 1369.000000 1369.000000 1369.000000 1369.000000 1.369000e+03 1369.000000
mean 1707.417471 1669.999993 1690.001155 1689.164074 4.350827e+06 1689.164074
std 858.422154 835.739754 848.009962 846.560097 2.103256e+06 846.560097
min 493.500000 474.000000 478.010010 482.070007 8.813000e+05 482.070007
25% 953.000000 939.210022 946.539978 946.940002 2.916200e+06 946.940002
50% 1694.239990 1646.310059 1672.540039 1668.949951 3.756200e+06 1668.949951
75% 1989.699951 1938.849976 1964.349976 1970.189941 5.155700e+06 1970.189941
max 3554.000000 3486.689941 3547.000000 3531.449951 1.656500e+07 3531.449951
In [7]:
fig = go.Figure(go.Scatter(
    x = df['Date'],
    y = df['Close']
))

fig.update_xaxes(
    rangeslider_visible=True,
    tickformatstops = [
        dict(dtickrange=[None, 1000], value="%H:%M:%S.%L ms"),
        dict(dtickrange=[1000, 60000], value="%H:%M:%S s"),
        dict(dtickrange=[60000, 3600000], value="%H:%M m"),
        dict(dtickrange=[3600000, 86400000], value="%H:%M h"),
        dict(dtickrange=[86400000, 604800000], value="%e. %b d"),
        dict(dtickrange=[604800000, "M1"], value="%e. %b w"),
        dict(dtickrange=["M1", "M12"], value="%b '%y M"),
        dict(dtickrange=["M12", None], value="%Y Y")
    ]
)
fig.update_xaxes(title="Date", title_font_size= 20)
fig.update_yaxes(title = "Closing Price", title_font_size= 20)
fig.update_layout(title_text="Analysis", title_font_size= 30)
fig.show()
In [8]:
fig = go.Figure(go.Scatter(
    x = df['Date'],
    y = df['Low'],
    name = 'Low Price'
))
fig.add_trace(go.Scatter(
    x = df['Date'],
    y = df['High'],
    mode = "lines",
    line=go.scatter.Line(color="black"),
    name = 'High Price'
))

fig.update_xaxes(
    rangeslider_visible=True,
    tickformatstops = [
        dict(dtickrange=[None, 1000], value="%H:%M:%S.%L ms"),
        dict(dtickrange=[1000, 60000], value="%H:%M:%S s"),
        dict(dtickrange=[60000, 3600000], value="%H:%M m"),
        dict(dtickrange=[3600000, 86400000], value="%H:%M h"),
        dict(dtickrange=[86400000, 604800000], value="%e. %b d"),
        dict(dtickrange=[604800000, "M1"], value="%e. %b w"),
        dict(dtickrange=["M1", "M12"], value="%b '%y M"),
        dict(dtickrange=["M12", None], value="%Y Y")
    ]
)
fig.update_xaxes(title="Date", title_font_size= 20)
fig.update_yaxes(title = "Price", title_font_size= 20)
fig.update_layout(title_text="Analysis", title_font_size= 30)
fig.show()
In [9]:
#scaling the data between the range 0 to 1.
scaler = MinMaxScaler(feature_range = (0,1))
scaled_data = scaler.fit_transform(df['Close'].values.reshape(-1,1))
In [10]:
#Taking the data from the past 60 to predict.
prediction_days = 60
In [11]:
#Converting the data into an array.
x_train = []
y_train = []
for x in range(prediction_days, len(scaled_data)):
    x_train.append(scaled_data[x-prediction_days:x, 0])
    y_train.append(scaled_data[x,0])

x_train, y_train = np.array(x_train), np.array(y_train)
x_train, y_train
Out[11]:
(array([[0.06356047, 0.05080377, 0.04975437, ..., 0.03308214, 0.03207209,
         0.0366599 ],
        [0.05080377, 0.04975437, 0.04938054, ..., 0.03207209, 0.0366599 ,
         0.03824384],
        [0.04975437, 0.04938054, 0.04127724, ..., 0.0366599 , 0.03824384,
         0.03658777],
        ...,
        [0.86296891, 0.85506563, 0.85250443, ..., 0.90245231, 0.88704591,
         0.89334554],
        [0.85506563, 0.85250443, 0.85584288, ..., 0.88704591, 0.89334554,
         0.8906532 ],
        [0.85250443, 0.85584288, 0.87022937, ..., 0.89334554, 0.8906532 ,
         0.91232977]]),
 array([0.03824384, 0.03658777, 0.03818153, ..., 0.8906532 , 0.91232977,
        0.91791772]))
In [12]:
#Reshaping the array.
x_train = np.reshape(x_train,(x_train.shape[0], x_train.shape[1], 1))
x_train.shape, y_train.shape
Out[12]:
((1309, 60, 1), (1309,))
In [13]:
#Building the lstm model.
lstm = Sequential()

lstm.add(LSTM(units = 50, return_sequences = True, input_shape = (x_train.shape[1],1)))
lstm.add(Dropout(0.2))
lstm.add(LSTM(units = 50, return_sequences = True))
lstm.add(Dropout(0.2))
lstm.add(LSTM(units = 50))
lstm.add(Dropout(0.2))
lstm.add(Dense(units = 1))

lstm.compile(optimizer = 'adam', loss = 'mean_squared_error')
lstm.fit(x_train, y_train, epochs = 100, batch_size =32, verbose = 2)

Epoch 1/100
41/41 - 30s - loss: 0.0301
Epoch 2/100
41/41 - 3s - loss: 0.0043
Epoch 3/100
41/41 - 2s - loss: 0.0036
Epoch 4/100
41/41 - 2s - loss: 0.0030
Epoch 5/100
41/41 - 2s - loss: 0.0030
Epoch 6/100
41/41 - 2s - loss: 0.0028
Epoch 7/100
41/41 - 2s - loss: 0.0030
Epoch 8/100
41/41 - 2s - loss: 0.0030
Epoch 9/100
41/41 - 3s - loss: 0.0027
Epoch 10/100
41/41 - 3s - loss: 0.0029
Epoch 11/100
41/41 - 3s - loss: 0.0028
Epoch 12/100
41/41 - 3s - loss: 0.0028
Epoch 13/100
41/41 - 3s - loss: 0.0024
Epoch 14/100
41/41 - 3s - loss: 0.0025
Epoch 15/100
41/41 - 3s - loss: 0.0023
Epoch 16/100
41/41 - 3s - loss: 0.0020
Epoch 17/100
41/41 - 3s - loss: 0.0025
Epoch 18/100
41/41 - 2s - loss: 0.0027
Epoch 19/100
41/41 - 3s - loss: 0.0020
Epoch 20/100
41/41 - 3s - loss: 0.0024
Epoch 21/100
41/41 - 2s - loss: 0.0020
Epoch 22/100
41/41 - 3s - loss: 0.0021
Epoch 23/100
41/41 - 2s - loss: 0.0020
Epoch 24/100
41/41 - 2s - loss: 0.0018
Epoch 25/100
41/41 - 2s - loss: 0.0020
Epoch 26/100
41/41 - 2s - loss: 0.0021
Epoch 27/100
41/41 - 2s - loss: 0.0020
Epoch 28/100
41/41 - 2s - loss: 0.0018
Epoch 29/100
41/41 - 2s - loss: 0.0018
Epoch 30/100
41/41 - 2s - loss: 0.0025
Epoch 31/100
41/41 - 3s - loss: 0.0019
Epoch 32/100
41/41 - 3s - loss: 0.0019
Epoch 33/100
41/41 - 3s - loss: 0.0020
Epoch 34/100
41/41 - 2s - loss: 0.0017
Epoch 35/100
41/41 - 2s - loss: 0.0019
Epoch 36/100
41/41 - 3s - loss: 0.0014
Epoch 37/100
41/41 - 3s - loss: 0.0017
Epoch 38/100
41/41 - 3s - loss: 0.0015
Epoch 39/100
41/41 - 2s - loss: 0.0018
Epoch 40/100
41/41 - 2s - loss: 0.0016
Epoch 41/100
41/41 - 3s - loss: 0.0016
Epoch 42/100
41/41 - 2s - loss: 0.0015
Epoch 43/100
41/41 - 3s - loss: 0.0016
Epoch 44/100
41/41 - 3s - loss: 0.0018
Epoch 45/100
41/41 - 3s - loss: 0.0015
Epoch 46/100
41/41 - 2s - loss: 0.0016
Epoch 47/100
41/41 - 3s - loss: 0.0015
Epoch 48/100
41/41 - 2s - loss: 0.0014
Epoch 49/100
41/41 - 3s - loss: 0.0015
Epoch 50/100
41/41 - 2s - loss: 0.0014
Epoch 51/100
41/41 - 3s - loss: 0.0015
Epoch 52/100
41/41 - 2s - loss: 0.0015
Epoch 53/100
41/41 - 2s - loss: 0.0014
Epoch 54/100
41/41 - 2s - loss: 0.0013
Epoch 55/100
41/41 - 2s - loss: 0.0014
Epoch 56/100
41/41 - 3s - loss: 0.0016
Epoch 57/100
41/41 - 2s - loss: 0.0016
Epoch 58/100
41/41 - 2s - loss: 0.0015
Epoch 59/100
41/41 - 3s - loss: 0.0015
Epoch 60/100
41/41 - 2s - loss: 0.0013
Epoch 61/100
41/41 - 2s - loss: 0.0012
Epoch 62/100
41/41 - 3s - loss: 0.0012
Epoch 63/100
41/41 - 2s - loss: 0.0013
Epoch 64/100
41/41 - 2s - loss: 0.0013
Epoch 65/100
41/41 - 2s - loss: 0.0012
Epoch 66/100
41/41 - 3s - loss: 0.0014
Epoch 67/100
41/41 - 3s - loss: 0.0013
Epoch 68/100
41/41 - 3s - loss: 0.0012
Epoch 69/100
41/41 - 3s - loss: 0.0012
Epoch 70/100
41/41 - 3s - loss: 0.0011
Epoch 71/100
41/41 - 3s - loss: 0.0010
Epoch 72/100
41/41 - 3s - loss: 0.0013
Epoch 73/100
41/41 - 2s - loss: 0.0012
Epoch 74/100
41/41 - 2s - loss: 0.0012
Epoch 75/100
41/41 - 2s - loss: 0.0011
Epoch 76/100
41/41 - 2s - loss: 0.0011
Epoch 77/100
41/41 - 2s - loss: 0.0012
Epoch 78/100
41/41 - 2s - loss: 0.0011
Epoch 79/100
41/41 - 2s - loss: 0.0011
Epoch 80/100
41/41 - 2s - loss: 9.3893e-04
Epoch 81/100
41/41 - 2s - loss: 0.0012
Epoch 82/100
41/41 - 2s - loss: 0.0010
Epoch 83/100
41/41 - 2s - loss: 9.9901e-04
Epoch 84/100
41/41 - 2s - loss: 0.0010
Epoch 85/100
41/41 - 2s - loss: 0.0010
Epoch 86/100
41/41 - 2s - loss: 0.0011
Epoch 87/100
41/41 - 2s - loss: 0.0011
Epoch 88/100
41/41 - 2s - loss: 0.0010
Epoch 89/100
41/41 - 2s - loss: 0.0011
Epoch 90/100
41/41 - 2s - loss: 0.0011
Epoch 91/100
41/41 - 3s - loss: 0.0010
Epoch 92/100
41/41 - 3s - loss: 0.0010
Epoch 93/100
41/41 - 3s - loss: 9.9506e-04
Epoch 94/100
41/41 - 3s - loss: 0.0010
Epoch 95/100
41/41 - 2s - loss: 0.0010
Epoch 96/100
41/41 - 2s - loss: 0.0011
Epoch 97/100
41/41 - 2s - loss: 0.0011
Epoch 98/100
41/41 - 2s - loss: 9.1929e-04
Epoch 99/100
41/41 - 2s - loss: 9.7446e-04
Epoch 100/100
41/41 - 2s - loss: 0.0011
Out[13]:
<keras.callbacks.History at 0x22486c60b20>
In [14]:
lstm.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, 60, 50)            10400     
_________________________________________________________________
dropout (Dropout)            (None, 60, 50)            0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 60, 50)            20200     
_________________________________________________________________
dropout_1 (Dropout)          (None, 60, 50)            0         
_________________________________________________________________
lstm_2 (LSTM)                (None, 50)                20200     
_________________________________________________________________
dropout_2 (Dropout)          (None, 50)                0         
_________________________________________________________________
dense (Dense)                (None, 1)                 51        
=================================================================
Total params: 50,851
Trainable params: 50,851
Non-trainable params: 0
_________________________________________________________________
In [15]:
#Now testing the Model Accuracy on Existing data by loading test data.
In [16]:
test_start = datetime(2020, 5, 1)
test_end = datetime.now()
In [17]:
stock_test = stock
In [18]:
test_data = pdr.get_data_yahoo(stock_test, test_start, test_end)
test_data = test_data.to_csv(f'{stock_test}_test.csv')
test_data = pd.read_csv(f'{stock_test}_test.csv')
test_data
Out[18]:
Date High Low Open Close Volume Adj Close
0 2020-04-30 2475.000000 2396.010010 2419.840088 2474.000000 9534600 2474.000000
1 2020-05-01 2362.439941 2258.189941 2336.800049 2286.040039 9772600 2286.040039
2 2020-05-04 2326.979980 2256.379883 2256.379883 2315.989990 4865900 2315.989990
3 2020-05-05 2351.000000 2307.129883 2340.000000 2317.800049 3242500 2317.800049
4 2020-05-06 2357.449951 2320.000000 2329.439941 2351.260010 3117800 2351.260010
... ... ... ... ... ... ... ...
275 2021-06-03 3214.439941 3184.030029 3204.229980 3187.010010 2398300 3187.010010
276 2021-06-04 3221.000000 3198.810059 3212.000000 3206.219971 2245700 3206.219971
277 2021-06-07 3208.000000 3172.199951 3197.330078 3198.010010 2215800 3198.010010
278 2021-06-08 3279.530029 3218.010010 3222.610107 3264.110107 3405900 3264.110107
279 2021-06-09 3297.580078 3270.699951 3272.870117 3281.149902 2439123 3281.149902

280 rows × 7 columns

In [19]:
actual_closing_prices = test_data['Close'].values
In [20]:
#linking actual data and test data.
total_dataset = pd.concat((df['Close'], test_data['Close']), axis = 0)
In [21]:
#Transform test data.
model_input_data = total_dataset[len(total_dataset)- len(test_data)-prediction_days:].values
model_input_data = model_input_data.reshape(-1,1)
model_input_data = scaler.transform(model_input_data)
In [22]:
x_test = []
for x in range(prediction_days, len(model_input_data)):
    x_test.append(model_input_data[x-prediction_days:x,0])
    
#coverting test data into array.
x_test = np.array(x_test)
x_test
Out[22]:
array([[0.85584288, 0.87022937, 0.8348976 , ..., 0.8906532 , 0.91232977,
        0.91791772],
       [0.87022937, 0.8348976 , 0.85030072, ..., 0.91232977, 0.91791772,
        0.6532246 ],
       [0.8348976 , 0.85030072, 0.86207693, ..., 0.91791772, 0.6532246 ,
        0.59158585],
       ...,
       [0.86296891, 0.85506563, 0.85250443, ..., 0.90245231, 0.88704591,
        0.89334554],
       [0.85506563, 0.85250443, 0.85584288, ..., 0.88704591, 0.89334554,
        0.8906532 ],
       [0.85250443, 0.85584288, 0.87022937, ..., 0.89334554, 0.8906532 ,
        0.91232977]])
In [23]:
#reshaping test data.
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
x_test.shape
Out[23]:
(280, 60, 1)
In [24]:
#Predictions on Test Data.
predicted_prices = lstm.predict(x_test)
predicted_prices = scaler.inverse_transform(predicted_prices)
In [25]:
df_predicted = pd.DataFrame(predicted_prices, columns=["Predicted Prices"])
df_test_data = pd.DataFrame(test_data, columns = ["Date"])

df_2 = [df_test_data['Date'], test_data['Close'], df_predicted['Predicted Prices'], ]
df_3 = pd.concat(df_2, axis = 1)
df_3
Out[25]:
Date Close Predicted Prices
0 2020-04-30 2474.000000 3297.869385
1 2020-05-01 2286.040039 3205.663086
2 2020-05-04 2315.989990 3025.325195
3 2020-05-05 2317.800049 2824.125732
4 2020-05-06 2351.260010 2651.706543
... ... ... ...
275 2021-06-03 3187.010010 3297.667480
276 2021-06-04 3206.219971 3288.975098
277 2021-06-07 3198.010010 3281.511475
278 2021-06-08 3264.110107 3275.760254
279 2021-06-09 3281.149902 3281.705322

280 rows × 3 columns

In [26]:
fig = go.Figure(go.Scatter(
    x = df_3['Date'],
    y = df_3['Close'],
name = 'Actual Price'))

fig.add_trace(go.Scatter(
    x = df_3['Date'],
    y = df_3['Predicted Prices'],
    mode = "lines",
    line=go.scatter.Line(color="black"),
    name = 'Predicted Price'
))
fig.update_xaxes(title="Date", title_font_size= 20)
fig.update_yaxes(title = "Price", title_font_size= 20)
fig.show()
In [27]:
#Predicting Next day.
real_data = [model_input_data[len(model_input_data)+1 - prediction_days: len(model_input_data)+1,0]]
real_data = np.array(real_data)
real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))
In [28]:
prediction = lstm.predict(real_data)
prediction = scaler.inverse_transform(prediction)
prediction

WARNING:tensorflow:Model was constructed with shape (None, 60, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 60, 1), dtype=tf.float32, name='lstm_input'), name='lstm_input', description="created by layer 'lstm_input'"), but it was called on an input with incompatible shape (None, 59, 1).
Out[28]:
array([[3297.8687]], dtype=float32)